Classification of neonatal jaundice in mobile application with noninvasive image processing methods

Hardalaç F., Aydın M., Kutbay U., Ayturan K., Akyel A., Caglar A., ...Daha Fazla

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, cilt.29, sa.4, ss.2116-2126, 2021 (SCI-Expanded, Scopus, TRDizin)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 4
  • Basım Tarihi: 2021
  • Doi Numarası: 10.3906/elk-2008-76
  • Dergi Adı: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.2116-2126
  • Anahtar Kelimeler: Neonatal&nbsp, jaundice&nbsp, Indirect hyperbilirubinemia, multiple&nbsp, regression analysis&nbsp, image interpretation, BILIRUBIN
  • Gazi Üniversitesi Adresli: Evet

Özet

This study aims a mobile support system to aid health care professionals in hospitals or in regions far away from hospitals to utilize noninvasive image processing methods for classification of neonatal jaundice. A considerably low processing cost is aimed to be attained by developing an algorithm that could work on a mobile device with low-end camera and processor capabilities within this study. In this context, an algorithm with low cost is developed performing detection of most meaningful parameters by a multiple input single output regression model and correlation.The advantage of the proposed method is that it can estimate bilirubin with the help of a simple regression curve. The reason for its low cost is that the noninvasive jaundice prediction is performed with a simple regression curve instead of many mathematical operations in morphological image processing methods. The study was performed on a total of 196 subjects, 61 of which were classified as severe jaundice while 95 of the newborns were mild jaundice cases, and other 40 cases are used for tests. As a result of this work, the two-group classification accuracy of the developed algorithm is observed to be 92.5% for the 40 subject test group.